The Price Is Not Right

Author(s):  
Robert Tillman

This chapter presents the argument that financialization, as a broad economic trend, has increased the opportunities for financial crime among firms both within and outside the financial services industry. The growth of the financial services industry, increasing dependence of many economies on financial services, increasing focus on share value by firms, and dramatic increases in compensation within the financial services industry have all contributed to increases in the frequency and scale of financial crime in recent years. To illustrate these trends, three case studies are reviewed: (1) the manipulation of electrical energy prices by investment bank subsidiaries; (2) the deliberate rigging of the London Interbank Offered Rate (Libor); and (3) the fixing of foreign exchange rates by investment bank traders. The case studies involve efforts by financial industry insiders to profit by manipulating the infrastructure of those markets, tinkering with the mechanisms by which prices and rates are set.

Author(s):  
Kishore Kumar Das ◽  
Shahnawaz Ali

This study aims to evaluate the effect of rapid changes in financial technologies and their impact on the financial services industry of India. A descriptive study has been made on the implementation of financial technologies in modern-day financial services Industries. An intensive literature review has been done from the existing most recent available journals, newspaper articles, government websites, and magazines. We have discussed the effects of the financial technologies on the existing financial system of India, the threats and challenges faced by the regulators in regulating the novel disruptive technologies. We have also discussed potential threats, challenges and future prospects of upcoming technologies in the mutual funds industry in India. Based on the literature review, it is found that financial technologies (FinTech) have a positive and important effect on the financial services industry of India. The AUM (Asset Under Management) has seen a tremendous jump in the recent past. It has also increased the customer experience with better access to the back office, even from remote places. In addition, the paper has also discussed the challenges faced by the regulators, who are yet to fully understand the implication of the fast-changing technological environment around us. Finally, this article contributes to the knowledge building and understanding of financial technologies and its impact on the financial industry, challenges, and future prospects.


Author(s):  
Vibha Bhandari

The banking and financial services industry today stands at the crossroads between the traditional methods of business and the evolving modern methods of banking and providing financial services. Technological advancement in the field of finance has led to a whole new form of doing business which is a radical departure from the conventional methods of doing business. The players in the field of banking and finance are facing challenges and competition from entities which never existed in the traditional sphere. The banking and financial industry is not only facing stiff competition from these new age players; it is also facing challenges to find fresh talent who can tide off this industry. This chapter in its existing form shall present an overview of the banking and financial industry of today in the wake of Industrial Revolution 4.0 and identify some associated challenges.


2005 ◽  
Vol 10 (4) ◽  
pp. 244-248 ◽  
Author(s):  
Alea Fairchild

PurposeThe purpose of this paper is to develop a better understanding of supply chain management in the financial services industry by examining information flow in improving efficiencies (i.e. material, information, capital, etc.) via intelligent matching.Design/methodology/approachThe objective was to address the issues specific to financial services organizations in intelligent matching, examining organizational, technological and application aspects. In order to address these issues, we have utilized an exploratory research methodology, based on a literature review and some initial case studies.FindingsDrivers for intelligent matching solutions have been suggested in this research to include the ability to link financial matching activities to other supply chain activities. Further integration of business processes, both within and between enterprises, will require both human and computational intelligence to approach efficiencies in automation.Research limitations/implicationsSupply chain efficiencies enabled by financial products and information give organizations greater visibility over their receivables, working capital needs, and overall financial position. Limitations of this research include a small sampling of case studies; future research will include a wider scope of cases as to test the findings.Practical implicationsInteroperability of information between the physical movement of goods and financial information within supply chains is key to realizing cost‐reduction and revenue‐enhancement advantages.Originality/valueThis paper discusses a potential area for extending our understanding of supply chains and the role of information flow in improving efficiencies, especially in the financial services industry.


2021 ◽  
Vol 275 ◽  
pp. 01042
Author(s):  
Langchen Liu

With the development of the times, the financial system is getting bigger and bigger, and the links between the various industries are getting closer, so we need to cluster the financial industry. But how to deal with is a problem, after thinking about comparison we found that we can make some treatment between them. So the purpose of this article is to analyze the spatial distribution characteristics of financial services industry clusters based on big data. Based on the experimental principle of data security, this paper processes some data that is known on the market and unknown within the enterprise, and simulates the experimental process by using the 4-model based V+ on big data evaluation, and then the experimental results are drawn. The experimental results show that our model can analyze the spatial distribution characteristics of industrial clusters by analyzing some characteristics of financial services enterprises.


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